Data Scientist II, RufusX Science UK

Amazon is hiring a Data Scientist II, RufusX Science UK in London, United Kingdom. Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include SageMaker, AI evaluation and NLP.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

SageMaker

ai-evaluationnlp

We are looking for a passionate, talented, and inventive Data Scientist with a strong machine learning and analytics background to help build industry-leading language technology powering Rufus, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey.

This innovative role focuses on developing conversation-based, multimodal shopping experiences, utilizing data analysis, statistical modeling, machine learning (ML) technologies, and experimentation to drive product decisions and optimize customer experiences.

Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data-driven insights to continuously improve our systems.

Key job responsibilities
As a Data Scientist, you will be responsible for the analysis, modeling, and optimization of AI technologies that will shape the future of shopping experiences. You will play a critical role in measuring and improving multimodal conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graphs, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include designing experiments, analyzing results, and launching new features, products and systems.

A day in the life
You will:
- Perform hands-on analysis and modeling of enormous multimodal datasets to develop insights into how to best help customers throughout their shopping journeys.
- Use statistical methods, machine learning, and data mining techniques to create scalable solutions for measuring and optimizing shopping assistant systems based on a rich set of structured and unstructured contextual signals.
- Design and analyze A/B tests and experiments to evaluate new features and model improvements, ensuring statistical rigor and actionable insights.
- Develop metrics, dashboards, and reporting frameworks to monitor system performance, customer engagement, and business impact.
- Build predictive models and conduct deep-dive analyses to identify opportunities for improving customer experience, conversion, and satisfaction.
- Collaborate with Applied Scientists and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment.
- Establish automated processes for large-scale data analysis, ETL pipelines, metric generation, and experimentation frameworks.
- Communicate results and insights to both technical and non-technical audiences, including through presentations, written reports, and data visualizations.

About the team
The Rufus Features Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI-driven shopping experiences at Amazon. The team works on every aspect of the Rufus AI, from making Rufus agentic, enabling customers to set price alerts or empower Rufus to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance. We utilize and advance state-of-art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

Basic qualifications

- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience in a ML or data scientist role with a large technology company
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience effectively communicating complex concepts through written and verbal communication
- Master's degree or above in Math, Statistics, Computer Science, or related science field

Preferred qualifications

- Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Data Scientist II, RufusX Science UK at Amazon rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data Scientist II, RufusX Science UK is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in GB, London.
The job was posted on June 17, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI EvaluationNLPSageMaker

Also common in data scientist jobs at this level

Spark PyTorch TensorFlow RAG MLOps scikit-learn

Pay

Typical data scientist job: $167k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

How do you decide that one model's output is better than another's for a given task?
What NLP problem have you worked on, and how did you measure whether it actually worked?
What are the limits of SageMaker that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Evaluation, NLP, and SageMaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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